Experimental investigations of two-phase flow measurement using ultrasonic sensors

This thesis presents the investigations conducted in the use of ultrasonic technology to measure two-phase flow in both horizontal and vertical pipe flows which is important for the petroleum industry. However, there are still key challenges to measure parameters of the multiphase flow accurately. Four methods of ultrasonic technologies were explored. The Hilbert-Huang transform (HHT) was first applied to the ultrasound signals of air-water flow on horizontal flow for measurement of the parameters of the two- phase slug flow. The use of the HHT technique is sensitive enough to detect the hydrodynamics of the slug flow. The results of the experiments are compared with correlations in the literature and are in good agreement. Next, experimental data of air-water two-phase flow under slug, elongated bubble, stratified-wavy and stratified flow regimes were used to develop an objective flow regime classification of two-phase flow using the ultrasonic Doppler sensor and artificial neural network (ANN). The classifications using the power spectral density (PSD) and discrete wavelet transform (DWT) features have accuracies of 87% and 95.6% respectively. This is considerably more promising as it uses non-invasive and non-radioactive sensors. Moreover, ultrasonic pulse wave transducers with centre frequencies of 1MHz and 7.5MHz were used to measure two-phase flow both in horizontal and vertical flow pipes. The liquid level measurement was compared with the conductivity probes technique and agreed qualitatively. However, in the vertical with a gas volume fraction (GVF) higher than 20%, the ultrasound signals were attenuated. Furthermore, gas-liquid and oil-water two-phase flow rates in a vertical upward flow were measured using a combination of an ultrasound Doppler sensor and gamma densitometer. The results showed that the flow gas and liquid flow rates measured are within ±10% for low void fraction tests, water-cut measurements are within ±10%, densities within ±5%, and void fractions within ±10%. These findings are good results for a relatively fast flowing multiphase flow

Two-phase slug flow measurement using ultra-sonic techniques in combination with T-Y junctions

The accurate measurement of multiphase flows of oil/water/gas is a critical element of oil exploration and production. Thus, over the last three decades; the development and deployment of in-line multiphase flow metering systems has been a major focus worldwide. Accurate measurement of multiphase flow in the oil and gas industry is difficult because there is a wide range of flow regimes and multiphase meters do not generally perform well under the intermittent slug flow conditions which commonly occur in oil production. This thesis investigates the use of Doppler and cross-correlation ultrasonic measurements made in different high gas void fraction flow, partially separated liquid and gas flows, and homogeneous flow and raw slug flow, to assess the accuracy of measurement in these regimes. This approach has been tested on water/air flows in a 50mm diameter pipe facility. The system employs a partial gas/liquid separation and homogenisation using a T-Y junction configuration. A combination of ultrasonic measurement techniques was used to measure flow velocities and conductivity rings to measure the gas fraction. In the partially separated regime, ultrasonic cross-correlation and conductivity rings are used to measure the liquid flow-rate. In the homogeneous flow, a clamp-on ultrasonic Doppler meter is used to measure the homogeneous velocity and combined with conductivity ring measurements to provide measurement of the liquid and gas flow-rates. The slug flow regime measurements employ the raw Doppler shift data from the ultrasonic Doppler flowmeter, together with the slug flow closure equation and combined with gas fraction obtained by conductivity rings, to determine the liquid and gas flow-rates. Measurements were made with liquid velocities from 1.0m/s to 2.0m/s with gas void fractions up to 60%. Using these techniques the accuracies of the liquid flow-rate measurement in the partially separated, homogeneous and slug regimes were 10%, 10% and 15% respectively. The accuracy of the gas flow-rate in both the homogeneous and raw slug regimes was 10%. The method offers the possibility of further improvement in the accuracy by combining measurement from different regimes

Image reconstruction technique for ultrasonic transmission tomography

Precise flow control has always been a necessity for developing easier approaches or instrumentation for two-phase flow regime. An important method for monitoring this process is called process tomography such as electrical tomography, optical tomography and ultrasonic tomography (UT). In the case of high-acoustic impedance mixtures e.g. bubbly flow, UT has the advantages in monitoring real time data. Although various researches were conducted using UT systems in bubbly flow regimes, there are still weaknesses especially in real time image reconstruction techniques for monitoring the process. Some efforts such as linear back projection (LBP), filter back projection (FBP), convolution back projection (CBP) and iterative techniques are utilized for reconstructing the image with few views data for UT system. Regardless of the utilized method there still exist two main issues in UT image reconstruction both in forward and inverse problems. In the case of forward problem, the gaps between sensitivity maps cause artifacts in a reconstructed image. Moreover, for inverse problem, limited number of sensors causes artifacts in reconstructed image. In the case of high noisy environment, the LBP, FBP and CBP methods are not capable of totally removing the noise and artifacts level. Dynamic motion of flow regime is considered as another issue in UT system which causes inaccuracy in image reconstruction. Therefore, these issues were considered in developing a modified image reconstruction algorithm which was based on improving the CBP algorithm both in forward and inverse problems. A modified sensitivity map based on Gaussian distribution was utilized to combat the gaps in forward problem, and for the case of inverse problem, the wavelet fusion technique was applied to reduce the noise level, artifacts and the effects of dynamic motions. The simulation and the experimental works had been conducted based on different static profiles. Various types of image reconstruction algorithms were implemented and compared with the proposed technique. The quality of the final reconstructed images was evaluated using structural similarity (SSIM) and peak signal to noise ratio (PSNR). Results show that the WCBP outperforms LBP and CBP in case of SSIM and PSNR. Comparing to LBP, the SSIM and PSNR were improved at least by 30% and 5% respectively while for CBP the improvement were about 5% and 1% respectively

Pseudo-image-feature-based identification benchmark for multi-phase flow regimes

Multiphase flow is a prevalent topic in many disciplines, and flow regime identification is an essential foundation in multiphase flow research. Computer vision and deep learning have achieved numerous excellent models, but many have not demonstrated satisfactory performance in fundamental research, including flow regime identification. This research proposes an advanced pseudo-image feature (PIF) as the flow regime descriptor and a benchmark of multiple deep learning classifiers. The PIF simulates the image format and compactly encodes the flow regime to a pseudo-image, which explicitly displays the implicit flow regime signals. This research further evaluates three proposed and five existing popular deep learning classifiers. The proposed benchmark provides a baseline for applying deep learning in flow regime identification. The proposed fully convolutional network (FCN) classifier achieved state-of-the-art performance, and the testing and verification accuracy respectively reached 99.95% and 99.54%. This research suggests that PIF has an excellent capability for flow regime representation, and the proposed deep learning classifiers achieve superior performance in flow regime identification compared to the existing classifiers. Industries can utilize the proposed multiphase flow identification technology to obtain greater production efficiency, productivity, and financial gai

Experimental study on mixing of activated sludge with or without air in a mixing tank through electrical resistance tomography (ERT) and response surface methodology

In such industrial processes as oxidation, hydrogenation, and biological fermentation gas and liquid are contacted and mixed to reach steady condition. The unit operations applied for gas/liquid processes include but are not limited to bubble column, plate column, mechanically agitated vessels, in-line static mixers, jet mixing devices/jet mixers, and surface aerators. Among aforementioned unit operations, aerated mixing vessels are mostly employed for the gas-liquid processes in such industry as biochemical, pharmaceutical, cosmetics, and waste water treatment. While gas-dispersion in Newtonian fluids, especially in low-viscosity systems, has been successfully understood, not enough information may be found in the literature about this process in non-Newtonian fluids. Therefore, in this study electrical resistance tomography (ERT) was utilized to assess the mixing of the activated sludge as shear thinning non-Newtonian fluid in presence of aeration. ERT results revealed that shorter mixing time can be achieved in presence of aeration. The following three central impellers were employed: ASI (a combination of A200 and the Scaba impellers), ARI (a combination of A200 and the Rushton impellers), and Rushton (fully radial impeller). An ERT system with a two-plane assembly equipped with 16 sensors on each plane was employed to assess the impact of the impeller type, impeller speed, and gas flow rate on the mixing of activated sludge in terms of mixing time, specific power consumption, and gas flow number. A statistical-based experimental design with RSM (response surface methodology) was applied to evaluate the individual and interactive effects of the design parameters and operating conditions. Experiments and RSM demonstrated that among all independent variables in this study, impeller speed was the common independent variable which impacts mixing time, specific power consumption and gas flow number significantly

Selected Papers from the 9th World Congress on Industrial Process Tomography

Industrial process tomography (IPT) is becoming an important tool for Industry 4.0. It consists of multidimensional sensor technologies and methods that aim to provide unparalleled internal information on industrial processes used in many sectors. This book showcases a selection of papers at the forefront of the latest developments in such technologies